SoroushVahidi/scidocs huggingface dataset:
Soft-Switching Solid-State Transformer (S4T)
(2018)

This paper presents a new topology for a fully bidirectional soft-switching solid-state transformer (S4T). The minimal topology, featuring 12 main devices and a high-frequency transformer, does not use an intermediate dc voltage link, and provides sinusoidal input and output voltages. The S4T can be configured to interface with two- or multiterminal dc, single- or multiphase ac systems. An auxiliary resonant circuit creates zero-voltage-switching conditions for main devices from no-load to full-load, and helps manage interactions with circuit parasitic elements. The modularized structure allows series and/or parallel stacking of converter cells for high-voltage and high-power applications.
Categorical equational systems : algebraic models and equational reasoning
(2010)

Declaration This dissertation is the result of my own work done under the guidance of my supervisor, and includes nothing which is the outcome of work done in collaboration except where specifically indicated in the text. This dissertation is not substantially the same as any that I have submitted or will be submitting for a degree or diploma or other qualification at this or any other University. This dissertation does not exceed the regulation length of 60,000 words, including tables and footnotes. 5 Summary We introduce two abstract notions of equational algebraic system, called Equational System (ES) and Term Equational System (TES), in order to achieve sufficient expressivity as needed in modern applications in computer science. These generalize the classical concept of (enriched) algebraic theory of Kelly and Power [1993]. We also develop a theory for constructing free algebras for ESs and a theory of equational reasoning for TESs. In Part I, we introduce the general abstract, yet practical, concept of equational system and develop finitary and transfinitary conditions under which we give an explicit construction of free algebras for ESs. This free construction extends the well-known construction of free algebras for ω-cocontinuous endofunctors to an equational setting, capturing the intuition that free algebras consist of freely constructed terms quotiented by given equations and congruence rules. We further show the monadicity and cocom-pleteness of categories of algebras for ESs under the finitary and transfinitary conditions. To illustrate the expressivity of equational systems, we exhibit various examples including two modern applications, the Σ-monoids of Fiore et al. [1999] and the π-algebras of Stark [2005]. In Part II, we introduce the more concrete notion of term equational system, which is obtained by specializing the concept of equational system, but remains more general than that of enriched algebraic theory. We first develop a sound logical deduction system, called Term Equational Logic (TEL), for equational reasoning about algebras of TESs. Then, to pursue a complete logic, we give an internal completeness result, from which together with the explicit construction of free algebras one can typically synthesize sound and complete rewriting-style equational logics. To exemplify this scenario, we give two applications: multi-sorted algebraic theories and nominal equational theories of Clouston and Pitts [2007] and of Gabbay and Mathijssen [2007]. 7 Acknowledgements First and foremost, I am deeply grateful to my Ph.D. supervisor Marcelo Fiore for his patient guidance and support, without which this thesis would …
A Novel Myoelectric Pattern Recognition Strategy for Hand Function Restoration After Incomplete Cervical Spinal Cord Injury
(2013)

This study presents a novel myoelectric pattern recognition strategy towards restoration of hand function after incomplete cervical spinal cord Injury (SCI). High density surface electromyogram (EMG) signals comprised of 57 channels were recorded from the forearm of nine subjects with incomplete cervical SCI while they tried to perform six different hand grasp patterns. A series of pattern recognition algorithms with different EMG feature sets and classifiers were implemented to identify the intended tasks of each SCI subject. High average overall accuracies (>; 97%) were achieved in classification of seven different classes (six intended hand grasp patterns plus a hand rest pattern), indicating that substantial motor control information can be extracted from partially paralyzed muscles of SCI subjects. Such information can potentially enable volitional control of assistive devices, thereby facilitating restoration of hand function. Furthermore, it was possible to maintain high levels of classification accuracy with a very limited number of electrodes selected from the high density surface EMG recordings. This demonstrates clinical feasibility and robustness in the concept of using myoelectric pattern recognition techniques toward improved function restoration for individuals with spinal injury.
Suicidal decapitation using a tractor loader: a case report and review of the literature.
(2007)

In forensic practice, decapitated bodies are predominantly associated with decapitation by wheels of trains or with postmortem dismemberment following homicide. In the suicidal context, decapitation accounts for less than 1% of total suicide. Apart from decapitation by trains, other encountered methods involve suicidal hanging and vehicle-assisted ligature suicide. Reported here is a unique case of suicidal decapitation in a 45-year-old man using a tractor loader at the foot of a silo, on his farm. The head was recovered in the loader and there were several impact spots from the loader as well as blood on the silo wall. The autopsy revealed a complete decapitation wound with the severance plane located between the third and fourth cervical vertebra. A 1.5 cm wide abrasion on the anterior part of the neck and abrasions under the chin were noted. This very unique case of intentional suicidal decapitation is the first reported case of a planned system intended to create decapitation outside the unique case of homemade guillotine and the more common decapitation by train.
Anatomical Basis for Safe and Effective Volumization of the Temple.
(2015)

BACKGROUND
One of the earliest but often unaddressed signs of facial aging is volume loss in the temple. Treatment of the area can produce satisfying results for both patient and practitioner.


OBJECTIVE
Safe injection requires explicit knowledge of the anatomy to avoid complications related to the multitude of vessels that course throughout the region at various depths. The authors aim to detail the anatomy of the area and provide a safe and easy-to-follow method for injection.


MATERIALS AND METHODS
The authors review the relevant anatomy of the temporal region and its application to cosmetic filler injections.


RESULTS
The authors describe an easy-to-follow approach for a safe and effective injection window based on numerous anatomical studies. Injection in this area is not without risk, including potential blindness. The authors review the potential complications and their treatments.


CONCLUSION
Hollowing of the temple is an early sign of aging that, when corrected, can lead to significant patient and practitioner satisfaction. Proper anatomically knowledge is required to avoid potentially severe complications. In this study, the authors present a reliable technique to safely and effectively augment this often undertreated area of the aging face.
Weakly Supervised Object Discovery by Generative Adversarial & Ranking Networks.
(2018)

The deep generative adversarial networks (GAN) recently have been shown to be promising for different computer vision applications, like image editing, synthesizing high resolution images, generating videos, etc. These networks and the corresponding learning scheme can handle various visual space mappings. We approach GANs with a novel training method and learning objective, to discover multiple object instances for three cases: 1) synthesizing a picture of a specific object within a cluttered scene; 2) localizing different categories in images for weakly supervised object detection; and 3) improving object discovery in object detection pipelines. A crucial advantage of our method is that it learns a new deep similarity metric, to distinguish multiple objects in one image. We demonstrate that the network can act as an encoder-decoder generating parts of an image which contain an object, or as a modified deep CNN to represent images for object detection in supervised and weakly supervised scheme. Our ranking GAN offers a novel way to search through images for object specific patterns. We have conducted experiments for different scenarios and demonstrate the method performance for object synthesizing and weakly supervised object detection and classification using the MS-COCO and PASCAL VOC datasets.
Translation techniques in cross-language information retrieval
(2012)

Cross-language information retrieval (CLIR) is an active sub-domain of information retrieval (IR). Like IR, CLIR is centered on the search for documents and for information contained within those documents. Unlike IR, CLIR must reconcile queries and documents that are written in different languages. The usual solution to this mismatch involves translating the query and/or the documents before performing the search. Translation is therefore a pivotal activity for CLIR engines. Over the last 15 years, the CLIR community has developed a wide range of techniques and models supporting free text translation. This article presents an overview of those techniques, with a special emphasis on recent developments.
A Review on Phishing Attacks and Various Anti Phishing Techniques
(2016)

Phishing is a threat that acquire sensitive information such as username, password etc through online. Phishing often takes place in email spoofing or instant messaging .Phishing email contains messages like ask the users to enter the personal information so that it is easy for hackers to hack the information. This paper presents an overview about various phishing attacks and various techniques to protect the information.
Open Government Data Implementation Evaluation
(2014)

This paper analyses the implementation of the Open Government Data strategy and portal of the City of Vienna. This evaluation is based on qualitative interviews and online polls after the strategy was implemented. Two groups of users were involved in the evaluation: internal target groups (employees and heads of department in the City of Vienna’s public administration departments) and external stakeholders (citizens, business representatives, science and research, journalists). Analyzed aspects included the present organizational processes, the benefits (to business and society), and requirements for future Open Government Data initiatives. This evaluation reveals success factors which accompanied the implementation: the clear definition of responsibilities and the implementation along a process model, the integration of the Open Government Data platform into existing Content Management Systems, the evaluation of the Open Government Data initiative very shortly after its inception. Based on the theoretical and empirical findings, recommendations for future Open Government Data strategies are made which target the local authority and would require action on the federal level such as Creative Commons Attribution License as the default for subsidy funds or public relation measures carried out directly by the data providing departments.
Geometric Correspondence Network for Camera Motion Estimation
(2018)

In this paper, we propose a new learning scheme for generating geometric correspondences to be used for visual odometry. A convolutional neural network (CNN) combined with a recurrent neural network (RNN) are trained together to detect the location of keypoints as well as to generate corresponding descriptors in one unified structure. The network is optimized by warping points from source frame to reference frame, with a rigid body transform. Essentially, learning from warping. The overall training is focused on movements of the camera rather than movements within the image, which leads to better consistency in the matching and ultimately better motion estimation. Experimental results show that the proposed method achieves better results than both related deep learning and hand crafted methods. Furthermore, as a demonstration of the promise of our method we use a naive SLAM implementation based on these keypoints and get a performance on par with ORB-SLAM.
An Efficient and Fast Li-Ion Battery Charging System Using Energy Harvesting or Conventional Sources
(2018)

This paper presents a multi-input battery charging system that is capable of increasing the charging efficiency of lithium-ion (Li-ion) batteries. The proposed battery charging system consists of three main building blocks: a pulse charger, a step-down dc–dc converter, and a power path controller. The pulse charger allows charging via a wall outlet or an energy harvesting system. It implements charge techniques that increase the battery charge efficiency of a Li-ion battery. The power path controller (PPC) functions as a power monitor and selects the optimal path for charging either via an energy harvesting system or an ac adapter. The step-down dc–dc converter provides an initial supply voltage to start up the energy harvesting system. The integrated circuit design is implemented on a CMOS 0.18 μm technology process. Experimental results verify that the proposed pulse charger reduces the charging time of 100 mAh and 45 mAh Li-ion batteries respectively by 37.35% and 15.56% and improves the charge efficiency by 3.15% and 3.27% when compared to the benchmark constant current-constant voltage charging technique. The step-down dc–dc converter has a maximum efficiency of 90% and the operation of the PPC is also verified by charging the battery via a thermoelectric energy harvesting system.
Automatic Question Generation using Discourse Cues
(2011)

In this paper, we present a system that automatically generates questions from natural language text using discourse connectives. We explore the usefulness of the discourse connectives for Question Generation (QG) that looks at the problem beyond sentence level. Our work divides the QG task into content selection and question formation. Content selection consists of finding the relevant part in text to frame question from while question formation involves sense disambiguation of the discourse connectives, identification of question type and applying syntactic transformations on the content. The system is evaluated manually for syntactic and semantic correctness.
Natural Language Processing methods and systems for biomedical ontology learning
(2011)

While the biomedical informatics community widely acknowledges the utility of domain ontologies, there remain many barriers to their effective use. One important requirement of domain ontologies is that they must achieve a high degree of coverage of the domain concepts and concept relationships. However, the development of these ontologies is typically a manual, time-consuming, and often error-prone process. Limited resources result in missing concepts and relationships as well as difficulty in updating the ontology as knowledge changes. Methodologies developed in the fields of Natural Language Processing, information extraction, information retrieval and machine learning provide techniques for automating the enrichment of an ontology from free-text documents. In this article, we review existing methodologies and developed systems, and discuss how existing methods can benefit the development of biomedical ontologies.
Predictive coding: a fresh view of inhibition in the retina.
(1982)

Interneurons exhibiting centre--surround antagonism within their receptive fields are commonly found in peripheral visual pathways. We propose that this organization enables the visual system to encode spatial detail in a manner that minimizes the deleterious effects of intrinsic noise, by exploiting the spatial correlation that exists within natural scenes. The antagonistic surround takes a weighted mean of the signals in neighbouring receptors to generate a statistical prediction of the signal at the centre. The predicted value is subtracted from the actual centre signal, thus minimizing the range of outputs transmitted by the centre. In this way the entire dynamic range of the interneuron can be devoted to encoding a small range of intensities, thus rendering fine detail detectable against intrinsic noise injected at later stages in processing. This predictive encoding scheme also reduces spatial redundancy, thereby enabling the array of interneurons to transmit a larger number of distinguishable images, taking into account the expected structure of the visual world. The profile of the required inhibitory field is derived from statistical estimation theory. This profile depends strongly upon the signal: noise ratio and weakly upon the extent of lateral spatial correlation. The receptive fields that are quantitatively predicted by the theory resemble those of X-type retinal ganglion cells and show that the inhibitory surround should become weaker and more diffuse at low intensities. The latter property is unequivocally demonstrated in the first-order interneurons of the fly's compound eye. The theory is extended to the time domain to account for the phasic responses of fly interneurons. These comparisons suggest that, in the early stages of processing, the visual system is concerned primarily with coding the visual image to protect against subsequent intrinsic noise, rather than with reconstructing the scene or extracting specific features from it. The treatment emphasizes that a neuron's dynamic range should be matched to both its receptive field and the statistical properties of the visual pattern expected within this field. Finally, the analysis is synthetic because it is an extension of the background suppression hypothesis (Barlow & Levick 1976), satisfies the redundancy reduction hypothesis (Barlow 1961 a, b) and is equivalent to deblurring under certain conditions (Ratliff 1965).
A SPEC RG Cloud Group's Vision on the Performance Challenges of FaaS Cloud Architectures
(2018)

As a key part of the serverless computing paradigm, Function-as-a-Service (FaaS) platforms enable users to run arbitrary functions without being concerned about operational issues. However, there are several performance-related issues surrounding the state-of-the-art FaaS platforms that can deter widespread adoption of FaaS, including sizeable overheads, unreliable performance, and new forms of the cost-performance trade-off. In this work we, the SPEC RG Cloud Group, identify six performance-related challenges that arise specifically in this FaaS model, and present our roadmap to tackle these problems in the near future. This paper aims at motivating the community to solve these challenges together.
Automatic navigation and landing of an indoor AR. drone quadrotor using ArUco marker and inertial sensors
(2017)

Within the next few years, unmanned quadrotors are likely to become an important vehicle in humans' daily life. However, their automatic navigation and landing in indoor environments are among the commonly discussed topics in this regard. In fact, the quadrotor should be able to automatically find the landing point from the nearby position, navigate toward it, and finally, land on it accurately and smoothly. In this paper, we proposed a low-cost and thorough solution to this problem by using both bottom-facing and front-facing cameras of the drone. In addition, in the case that vision data were unavailable, inertial measurements alongside a Kalman filter were used to navigate the drone to achieve the promising continuity and reliability. An AR.Drone 2.0 quadrotor, as well as an ArUco marker, were employed in order to test the proposed method experimentally. The results indicated that the drone successfully landed on the predefined position with an acceptable time and accuracy.
Improved Neural Machine Translation with SMT Features
(2016)

Neural machine translation (NMT) conducts end-to-end translation with a source language encoder and a target language decoder, making promising translation performance. However, as a newly emerged approach, the method has some limitations. An NMT system usually has to apply a vocabulary of certain size to avoid the time-consuming training and decoding, thus it causes a serious out-of-vocabulary problem. Furthermore, the decoder lacks a mechanism to guarantee all the source words to be translated and usually favors short translations, resulting in fluent but inadequate translations. In order to solve the above problems, we incorporate statistical machine translation (SMT) features, such as a translation model and an n-gram language model, with the NMT model under the log-linear framework. Our experiments show that the proposed method significantly improves the translation quality of the state-of-the-art NMT system on Chinese-toEnglish translation tasks. Our method produces a gain of up to 2.33 BLEU score on NIST open test sets.
MARSSx 86 : A Full System Simulator for x 86 CPUs
(2011)

We present MARSS, an open source, fast, full system simulation tool built on QEMU to support cycle-accurate simulation of superscalar homogeneous and heterogeneous multicore x86 processors. MARSS includes detailed models of coherent caches, interconnections, chipsets, memory and IO devices. MARSS simulates the execution of all software components in the system, including unmodified binaries of applications, OS and libraries.
Automatic programming assessment and test data generation a review on its approaches
(2010)

Automatic programming assessment has recently become an important method in assisting lecturers and instructors of programming courses to automatically mark and grade students' programming exercises as well as to provide useful feedbacks on students' programming solutions. As part of the method, test data generation process plays as an integral part to perform a dynamic testing on students' programs. To date, various automated methods for test data generation particularly in software testing field are available. Unfortunately, they are seldom used in the context of automatic programming assessment research area. Nevertheless, there have been limited studies taking a stab to integrate both of them due to more useful features and to include a better quality program testing coverage. Thus, this paper provides a review on approaches that have been implemented in various studies with regard to automatic programming assessment, test data generation and integration of both of them. This review is aimed at gathering different techniques that have been employed to forward an interested reader the starting points for finding further information regarding its trends. In addition, the result of the review reveals the main gap that exists within the context of the considered areas which contributes to our main research topic of interest.
Learning Important Features Through Propagating Activation Differences
(2017)

The purported “black box” nature of neural networks is a barrier to adoption in applications where interpretability is essential. Here we present DeepLIFT (Deep Learning Important FeaTures), a method for decomposing the output prediction of a neural network on a specific input by backpropagating the contributions of all neurons in the network to every feature of the input. DeepLIFT compares the activation of each neuron to its ‘reference activation’ and assigns contribution scores according to the difference. By optionally giving separate consideration to positive and negative contributions, DeepLIFT can also reveal dependencies which are missed by other approaches. Scores can be computed efficiently in a single backward pass. We apply DeepLIFT to models trained on MNIST and simulated genomic data, and show significant advantages over gradient-based methods. Video tutorial: http://goo.gl/ qKb7pL, code: http://goo.gl/RM8jvH.